Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., “localization”). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays. In this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies. Simulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by >50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models. Movement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

中文翻译：

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基于声学遥测数据的运动辅助定位。

声学遥测技术正越来越多地用于研究各种水生类群，包括鱼类、爬行动物和海洋哺乳动物。大型合作遥测网络产生大量数据，可用于研究运动、资源选择和物种分布。声学遥测数据的有效使用需要根据接收器的检测来估计声源位置（即“定位”）。多个过程为定位估计提供信息，包括接收器处的检测/未检测数据、信号速率信息以及描述个人如何移动和利用空间的基础运动模型。然而，定位方法通常只集成这些过程的一个子集，并没有利用接收器阵列提供的完整空间遭遇历史信息。在本文中，我们在声学遥测定位的挑战和新开发的空间捕获-再捕获 (SCR) 方法之间进行了类比。我们基于声学遥测空间遭遇历史数据开发了一个定位框架，该框架集成了运动、信号（或提示）率和检测概率的显式子模型。这种我们称之为运动辅助定位的方法有效地利用了声学接收器阵列中可用的完整遭遇历史数据，提供了少于三个检测的定位，甚至允许预测个人的位置，当它根本没有被发现。我们通过开发 SCR 运动辅助定位模型的可推广贝叶斯公式来证明这些概念，以解决声学遥测研究中常见的研究特定挑战。仿真研究表明，与仅使用给定信号的检测历史的定位相比，运动辅助定位模型将定位估计的逐点 RMSE 提高了 >50%，并大大提高了估计轨迹的精度。此外，集成信号速率子模型减少了在独立定位模型中观察到的运动、信号速率和检测参数的估计偏差。运动辅助定位提供了一个灵活的框架，以最大限度地利用声学遥测数据。